{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image features exercise\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.\n",
    "\n",
    "All of your work for this exercise will be done in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "Similar to previous exercises, we will load CIFAR-10 data from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cs231n.features import color_histogram_hsv, hog_feature\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Features\n",
    "For each image we will compute a Histogram of Oriented\n",
    "Gradients (HOG) as well as a color histogram using the hue channel in HSV\n",
    "color space. We form our final feature vector for each image by concatenating\n",
    "the HOG and color histogram feature vectors.\n",
    "\n",
    "Roughly speaking, HOG should capture the texture of the image while ignoring\n",
    "color information, and the color histogram represents the color of the input\n",
    "image while ignoring texture. As a result, we expect that using both together\n",
    "ought to work better than using either alone. Verifying this assumption would\n",
    "be a good thing to try for your interests.\n",
    "\n",
    "The `hog_feature` and `color_histogram_hsv` functions both operate on a single\n",
    "image and return a feature vector for that image. The extract_features\n",
    "function takes a set of images and a list of feature functions and evaluates\n",
    "each feature function on each image, storing the results in a matrix where\n",
    "each column is the concatenation of all feature vectors for a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "from cs231n.features import *\n",
    "\n",
    "num_color_bins = 10 # Number of bins in the color histogram\n",
    "feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n",
    "X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n",
    "X_val_feats = extract_features(X_val, feature_fns)\n",
    "X_test_feats = extract_features(X_test, feature_fns)\n",
    "\n",
    "# Preprocessing: Subtract the mean feature\n",
    "mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats -= mean_feat\n",
    "X_val_feats -= mean_feat\n",
    "X_test_feats -= mean_feat\n",
    "\n",
    "# Preprocessing: Divide by standard deviation. This ensures that each feature\n",
    "# has roughly the same scale.\n",
    "std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats /= std_feat\n",
    "X_val_feats /= std_feat\n",
    "X_test_feats /= std_feat\n",
    "\n",
    "# Preprocessing: Add a bias dimension\n",
    "X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n",
    "X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n",
    "X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train SVM on features\n",
    "Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-09 reg 5.000000e+04 train accuracy: 0.092735 val accuracy: 0.102000\n",
      "lr 1.000000e-09 reg 5.000000e+05 train accuracy: 0.110082 val accuracy: 0.110000\n",
      "lr 1.000000e-09 reg 5.000000e+06 train accuracy: 0.150918 val accuracy: 0.148000\n",
      "lr 1.000000e-08 reg 5.000000e+04 train accuracy: 0.095449 val accuracy: 0.094000\n",
      "lr 1.000000e-08 reg 5.000000e+05 train accuracy: 0.357612 val accuracy: 0.346000\n",
      "lr 1.000000e-08 reg 5.000000e+06 train accuracy: 0.414571 val accuracy: 0.420000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.412327 val accuracy: 0.416000\n",
      "lr 1.000000e-07 reg 5.000000e+05 train accuracy: 0.413020 val accuracy: 0.419000\n",
      "lr 1.000000e-07 reg 5.000000e+06 train accuracy: 0.373224 val accuracy: 0.353000\n",
      "best validation accuracy achieved during cross-validation: 0.420000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune the learning rate and regularization strength\n",
    "\n",
    "from cs231n.classifiers.linear_classifier import LinearSVM\n",
    "\n",
    "learning_rates = [1e-9, 1e-8, 1e-7]\n",
    "regularization_strengths = [5e4, 5e5, 5e6]\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_svm = None\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained classifer in best_svm. You might also want to play          #\n",
    "# with different numbers of bins in the color histogram. If you are careful    #\n",
    "# you should be able to get accuracy of near 0.44 on the validation set.       #\n",
    "################################################################################\n",
    "for lr in learning_rates:\n",
    "    for reg in regularization_strengths:\n",
    "        svm = LinearSVM()\n",
    "        svm.train(X_train_feats, y_train, learning_rate=lr, reg=reg, num_iters=1500,\n",
    "                  batch_size=200, verbose=False)\n",
    "        pred_train = svm.predict(X_train_feats)\n",
    "        train_acc = (pred_train == y_train).mean()\n",
    "        pred_val = svm.predict(X_val_feats)\n",
    "        val_acc = (pred_val == y_val).mean()\n",
    "        results[(lr, reg)] = (train_acc, val_acc)\n",
    "        if val_acc > best_val:\n",
    "            best_val = val_acc\n",
    "            best_svm = svm\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.423\n"
     ]
    }
   ],
   "source": [
    "# Evaluate your trained SVM on the test set\n",
    "y_test_pred = best_svm.predict(X_test_feats)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print(test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28963711908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An important way to gain intuition about how an algorithm works is to\n",
    "# visualize the mistakes that it makes. In this visualization, we show examples\n",
    "# of images that are misclassified by our current system. The first column\n",
    "# shows images that our system labeled as \"plane\" but whose true label is\n",
    "# something other than \"plane\".\n",
    "\n",
    "examples_per_class = 8\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for cls, cls_name in enumerate(classes):\n",
    "    idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n",
    "    idxs = np.random.choice(idxs, examples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n",
    "        plt.imshow(X_test[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 1:\n",
    "Describe the misclassification results that you see. Do they make sense?\n",
    "\n",
    "基于color histogram和HOG特征来进行分类，所以物体背景的颜色和物体本身轮廓会造成很大的影响，例如蓝色背景多的图像更容易误认为飞机，动物猫和狗的轮廓相似，也容易误识别。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network on image features\n",
    "Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels. \n",
    "\n",
    "For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 155)\n",
      "(49000, 154)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: Remove the bias dimension\n",
    "# Make sure to run this cell only ONCE\n",
    "print(X_train_feats.shape)\n",
    "X_train_feats = X_train_feats[:, :-1]\n",
    "X_val_feats = X_val_feats[:, :-1]\n",
    "X_test_feats = X_test_feats[:, :-1]\n",
    "\n",
    "print(X_train_feats.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr:3.0000 reg:0.001000 train_acc:0.9750 val_acc:0.5650\n",
      "lr:3.0000 reg:0.000300 train_acc:0.9800 val_acc:0.5580\n",
      "lr:3.0000 reg:0.000500 train_acc:0.9750 val_acc:0.5590\n",
      "lr:3.0000 reg:0.000700 train_acc:0.9750 val_acc:0.5840\n",
      "lr:3.0000 reg:0.000100 train_acc:0.9750 val_acc:0.5550\n",
      "lr:2.5000 reg:0.001000 train_acc:0.9650 val_acc:0.5690\n",
      "lr:2.5000 reg:0.000300 train_acc:0.9900 val_acc:0.5700\n",
      "lr:2.5000 reg:0.000500 train_acc:0.9900 val_acc:0.5760\n",
      "lr:2.5000 reg:0.000700 train_acc:0.9700 val_acc:0.5740\n",
      "lr:2.5000 reg:0.000100 train_acc:0.9950 val_acc:0.5470\n",
      "lr:2.0000 reg:0.001000 train_acc:0.9400 val_acc:0.5870\n",
      "lr:2.0000 reg:0.000300 train_acc:0.9850 val_acc:0.5940\n",
      "lr:2.0000 reg:0.000500 train_acc:0.9850 val_acc:0.5830\n",
      "lr:2.0000 reg:0.000700 train_acc:0.9950 val_acc:0.5870\n",
      "lr:2.0000 reg:0.000100 train_acc:1.0000 val_acc:0.5730\n",
      "lr:1.5000 reg:0.001000 train_acc:0.9650 val_acc:0.6060\n",
      "lr:1.5000 reg:0.000300 train_acc:0.9950 val_acc:0.5780\n",
      "lr:1.5000 reg:0.000500 train_acc:0.9950 val_acc:0.5840\n",
      "lr:1.5000 reg:0.000700 train_acc:0.9850 val_acc:0.5970\n",
      "lr:1.5000 reg:0.000100 train_acc:1.0000 val_acc:0.5640\n",
      "lr:1.0000 reg:0.001000 train_acc:0.9800 val_acc:0.5970\n",
      "lr:1.0000 reg:0.000300 train_acc:1.0000 val_acc:0.5870\n",
      "lr:1.0000 reg:0.000500 train_acc:1.0000 val_acc:0.5830\n",
      "lr:1.0000 reg:0.000700 train_acc:1.0000 val_acc:0.5960\n",
      "lr:1.0000 reg:0.000100 train_acc:1.0000 val_acc:0.5750\n",
      "lr:0.1000 reg:0.001000 train_acc:1.0000 val_acc:0.5910\n",
      "lr:0.1000 reg:0.000300 train_acc:1.0000 val_acc:0.5980\n",
      "lr:0.1000 reg:0.000500 train_acc:0.9950 val_acc:0.6070\n",
      "lr:0.1000 reg:0.000700 train_acc:0.9950 val_acc:0.6050\n",
      "lr:0.1000 reg:0.000100 train_acc:1.0000 val_acc:0.6070\n"
     ]
    }
   ],
   "source": [
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "input_dim = X_train_feats.shape[1]\n",
    "hidden_dim = 512  #200\n",
    "num_classes = 10\n",
    "\n",
    "net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "best_net = None\n",
    "best_val = -1\n",
    "results = {} \n",
    "learning_rate = np.array([30, 25, 20, 15, 10, 1])*1e-1\n",
    "regularization = np.array([0.001, 0.0003, 0.0005, 0.0007, 0.0001])\n",
    "\n",
    "################################################################################\n",
    "# TODO: Train a two-layer neural network on image features. You may want to    #\n",
    "# cross-validate various parameters as in previous sections. Store your best   #\n",
    "# model in the best_net variable.                                              #\n",
    "################################################################################\n",
    "for lr in learning_rate:\n",
    "    for reg in regularization:\n",
    "        stats = net.train(X_train_feats, y_train, X_val_feats, y_val,\n",
    "                         num_iters=3000, batch_size=200,\n",
    "                         learning_rate=lr, learning_rate_decay=0.95, reg=reg, verbose=False)\n",
    "        train_acc = stats['train_acc_history'][-1]\n",
    "        val_acc = stats['val_acc_history'][-1]\n",
    "        info = 'lr:{:.4f} reg:{:.6f} train_acc:{:.4f} val_acc:{:.4f}'.format(lr, reg, train_acc, val_acc)\n",
    "        print(info)\n",
    "        if val_acc > best_val:\n",
    "            best_val = val_acc\n",
    "            best_net = net\n",
    "            results[(lr, reg)] = (train_acc, val_acc)\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.1, 0.0005) (0.995, 0.607)\n"
     ]
    }
   ],
   "source": [
    "acc = list(results.values())\n",
    "arr = np.array(acc)\n",
    "# np.argmax(a, axis=0)\n",
    "# print(arr)\n",
    "max_index = np.argmax(arr, axis=0)[1]\n",
    "max_key = list(results.keys())[max_index]\n",
    "print(max_key, results[max_key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.59\n"
     ]
    }
   ],
   "source": [
    "# Run your best neural net classifier on the test set. You should be able\n",
    "# to get more than 55% accuracy.\n",
    "\n",
    "test_acc = (best_net.predict(X_test_feats) == y_test).mean()\n",
    "print(test_acc)"
   ]
  }
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